{"id":46265,"date":"2018-12-29T11:27:25","date_gmt":"2018-12-29T05:57:25","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=46265"},"modified":"2021-12-07T11:18:00","modified_gmt":"2021-12-07T05:48:00","slug":"business-intelligence-and-data-warehousing","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/business-intelligence-and-data-warehousing\/","title":{"rendered":"Business Intelligence and Data Warehousing – Data Warehouse Concepts"},"content":{"rendered":"

Lastly, we discussed Business Intelligence Tools<\/a><\/strong>. Today, we will see the correlation Business Intelligence and Data Warehousing.<\/span><\/p>\n

In our attempt to learning Business Intelligence and its aspect, we must learn the important technology i.e. data warehousing.<\/span><\/p>\n

In this lesson, we will learn both the concepts of business Intelligence and data warehousing. <\/span><\/p>\n

Also, we will see how they work in tandem as well. <\/span>Moreover, we will look at components of data warehouse and data warehouse architecture.\u00a0<\/span><\/p>\n

So, let’s start Business Intelligence and Data Warehousing Tutorial.<\/p>\n

What is Business Intelligence?<\/h3>\n

The term Business Intelligence refers<\/a><\/strong> collectively to the tools and technologies used for the collection, integration, analysis, and visualization of data. <\/span><\/p>\n

The raw data which we collect from different data sources\u00a0transform into comprehensible data or meaningful information using BI technologies.<\/span><\/p>\n

This information interprets strategically by looking for trends and patterns in order to make business decision supported by facts revealed by the analyzed data. <\/span><\/p>\n

To simplify the concept, we collect raw data from various sources and with the help of Business Intelligence tools transform it into meaningful information. We can store such data in data files, databases, data warehouses or data lakes in specific data structures.<\/span><\/p>\n

From the data warehouses, we can retrieve stored data in the form of a report, query, make a dashboard to conduct data analysis. We do this with the process known as ETL (Extract, Transform, Load). Thus, enterprise executive can use the extracted, transformed and loaded data on different levels.<\/span><\/p>\n

He uses this to draw insights and fuel their decision making with the useful insights revealed by analyzing the data. <\/span><\/p>\n

What is Data Warehousing?<\/h3>\n

Data warehousing is the process of storing data in data warehouses, which are databases following the relational database model. Data is selected from different data sources, aggregated, organized and managed to provide meaningful insights into data for analysis & queries.<\/span><\/p>\n

A holistic approach to deal with and manage immense amounts of data that we use at enterprise levels.<\/span><\/p>\n

In such a wholesome approach, data does not simply fetches from data sources for operational or transactional tasks but transform in a certain way that we use for analytical and comparison purposes. <\/span><\/p>\n

A data warehouse is known by several other terms like <\/span>Decision Support System (DSS)<\/span><\/i>, <\/span>Executive Information System<\/span><\/i>, <\/span>Management Information System<\/span><\/i>, <\/span>Business Intelligence Solution<\/span><\/i>, <\/span>Analytic Application<\/span><\/i>. <\/span><\/p>\n

We call it Decision Support System as it provides useful insights and patterns shown by data as a result of the analysis which makes taking important decisions in business easy and safe. <\/span><\/p>\n

Therefore, in almost all the enterprises, a data warehouse maintains separately from the operational database. We use it only for transactional purposes which is more objective in nature.<\/span><\/p>\n

Have a look at Qlik Sense in BI Tools<\/a><\/strong><\/p>\n

How does Data Warehousing Work?<\/h3>\n

In a normal operational database are fully normalized data or is in the third normal form (3NF). In a 3NF state, every field of the table in a database is functionally dependent on only the primary key and does not contain any indirect associations.<\/span><\/p>\n

This means highly ramify data and so fetching data in such a condition is a slow process. In data warehousing<\/a>, data is de-normalized i.e. it is converted to 2NF from 3NF and hence, is called Big data<\/strong><\/a>. <\/span><\/p>\n

We call it big data because of data redundancy increases and so, data size increases. The sole purpose of creating data warehouses is to retrieve processed data quickly. <\/span><\/p>\n

Also, to provide aggregate data like totals, averages, general trends etc for enterprises to analyze and make decisions good for their business and functioning in the industry. <\/span><\/p>\n

Components of Data Warehouse<\/h3>\n

A data warehouse has several components that work in tandem to make data warehousing possible. <\/span><\/p>\n